Optimizing Ultrasonication Parameters for the Delivery of Graphene-Based Nano E-Tattoos in Continuous Health Monitoring
Presenter: Gianna Elie
Faculty Sponsor: Dmitry Kireev
School: UMass Amherst
Research Area: Biomedical Engineering
Session: Poster Session 4, 2:15 PM - 3:00 PM, Auditorium, A79
ABSTRACT
Continuous and non-invasive health monitoring is essential for managing chronic diseases, yet existing wearable sensors often lack the biocompatibility and longevity required for everyday use. This study investigates the development of graphene-based nano e-tattoos which are ultra-thin, conductive sensors designed to monitor physiological signals for up to ten days. Despite the potential of these devices, a significant challenge remains in achieving a reproducible and safe delivery method that ensures consistent electrical performance without damaging biological tissue. The research employs a mixed-methods experimental approach to optimize an ultrasonication-based delivery system. By embedding conductive nanomaterials into a biocompatible polymer matrix of sodium alginate and chitosan, the study investigates how variables such as probe geometry (2 mm vs. 12 mm), pulse cycles, and probe-to-skin distance influence ink penetration and signal retention. Quantitative data was gathered through resistance and impedance measurements, while qualitative histological analysis was used to assess skin integrity and cytotoxicity. The study’s hypotheses suggest that transitioning from manual probe application to a standardized, stationary pulse-mode method can significantly reduce human error and improve electrical conductivity. Preliminary results indicate that while closer probe proximity (1 mm) minimizes electrical resistance, it increases the risk of thermal buildup. Conversely, a 3 mm distance between the probe and skin provides superior structural definition and reproducibility. This research offers a deeper understanding of the mechanical-biological interface, providing a framework for public health technology to move toward more resilient, patient-friendly monitoring systems. By optimizing these delivery parameters, researchers can create more effective tools for early disease detection in an evolving healthcare landscape.
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